Presentation on theme: "1 Grid Modernization – A Strategic Imperative for 2050 Advanced Energy Conference May 1, 2013 By Carl Imhoff Electric Infrastructure Sector Manager Pacific."— Presentation transcript:
1 Grid Modernization – A Strategic Imperative for 2050 Advanced Energy Conference May 1, 2013 By Carl Imhoff Electric Infrastructure Sector Manager Pacific Northwest National Lab
The challenge ahead is complex The grid must meet new expectations 2 2 Electrify transportation sector to reduce dependence on imported oil Delivering 300GW of renewable generation by 2025 Maximize benefits of end-use efficiency and storage Accommodate changing and responsive loads Historical Expectations Emerging Expectations Affordable Power Reliable Power Secure Power Our electric infrastructure must continue to deliver affordable, reliable and secure power while simultaneously undergoing a major transformation
Transformation is Already Substantial North American phasor measurement network will exceed 1200 measurement points networked by late 2013 Digital metering expected to reach 30% market share 2013, 50% by Mainstay operational tools (minutes) now being demonstrated at SCADA rate (seconds) Distribution automation demonstrating substantial improvements in efficiency and reliability Demand response at the GW scale in several markets in the U.S. Challenge: How do we capture Smart Grid benefits in current grid AND position to enable new paradigms for the grid we want in 2050?
Future Outcomes Enabled by Grid Transformation Continued digitization across the system will lead to a “transactive” future that delivers broad optimization Level playing field for legacy and new “smart” infrastructure Leverages broad transparency to engage demand to help manage reliability and deliver clean generation Enables consumers to engage their energy choices like never before Increased strategic value of grid for public goods issues leverage “transactive management” Energy efficiency, Preferred fuel / generation mix etc. Electrification of transportation Broad use of high performance computing at local and regional levels to enable new paradigms of design and operation, delivering new levels of resilience to all hazards
Pacific Northwest Demonstration Project 5 What: $178M, ARRA-funded, 5-year demonstration 60,000 metered customers in 5 states Why: Quantify costs and benefits Develop communications protocol Develop standards Facilitate integration of wind and other renewables Who: Led by Battelle and partners including BPA, 11 utilities, 2 universities, and 5 vendors
6 PNWSG Demo Project Basics Operational objectives Manage peak demand Facilitate renewable resources Address constrained resources Improve system reliability and efficiency Select economical resources (optimize the system) Aggregation of Power and Signals Occurs Through a Hierarchy of Interfaces
8 Regional Modeling Alstom EMS Alstom MMS Future state Estimation by optimization Alstom MMS Future state Estimation by optimization BPA 3TIER Load Forecast Generation Schedules Outages Renewable Generation Forecasts Network State Gen. schedules Load forecasts Transmission Zone TC Node Inputs
High Performance Computing Opens New Paradigms of Operation Massive Contingency Analysis: HPC improving reliability and efficiency of power systems operations Parallelization dramatically increases computational speed Enables evaluation of a large number of scenarios Revolutionizes grid operations and planning Fast Dynamic Simulation: New model improving system efficiency Full topology model Real-time performance rating Enables improved asset management 9 # of processorsSpeedup Currently running 10k processors, achieved 10,000x speed up
Parallelized Optimization Methods Enable Precise Management of Grid Complexities WECC 230kV and above Serial NDS is faster than CPLEX Parallel NDS is 17 times faster WECC 100kV and above CPLEX no longer practical— time is divided by 10 and not converged Parallel NDS is 160 times faster—even ignoring CPLEX pre-solve time The bigger the problem, the better the relative performance The bigger the problem, the better the relative performance
Key Challenge: Data to Knowledge We need: New networks to route data securely and efficiently Distributed signal analysis and hardware automation New analytic methods to extract knowledge Simulations that run in microseconds vs. minutes, minutes vs. days Visual analytics to aid decision making Counter measures to advanced, persistent threats The major challenge is in translating new real-time data into actionable knowledge that enables operation of the system in ways never before possible – ensuring unprecedented Reliability, Resilience and Efficiency.
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